Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning Sequential Decision Rules Using Simulation Models and Competition
Machine Learning - Special issue on genetic algorithms
Biases in the Crossover Landscape
Proceedings of the 3rd International Conference on Genetic Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A Diploid Genetic Algorithm for Preserving Population Diversity - pseudo-Meiosis GA
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
The behavior of adaptive systems which employ genetic and correlation algorithms
The behavior of adaptive systems which employ genetic and correlation algorithms
Nonlinearities in genetic adaptive search.
Nonlinearities in genetic adaptive search.
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
A comparison of the fixed and floating building block representation in the genetic algorithm
Evolutionary Computation
Putting more genetics into genetic algorithms
Evolutionary Computation
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On location independent representations and self-organization
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Emergence of genomic self-similarity in location independent representations
Genetic Programming and Evolvable Machines
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using over-sampling in a Bayesian classifier EDA to solve deceptive and hierarchical problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Effective vaccination policies
Information Sciences: an International Journal
Automated abstract planning with use of genetic algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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We introduce a genetic algorithm (GA) with a new representation method which we call the proportional GA (PGA). The PGA is a multi-character GA that relies on the existence or non-existence of genes to determine the information that is expressed. The information represented by a PGA individual depends only on what is present on the individual and not on the order in which it is present. As a result, the order of the encoded information is free to evolve in response factors other than the value of the solution, for example, in response to the identification and formation of building blocks. The PGA is also able to dynamically evolve the resolution of encoded information. In this paper, we describe our motivations for developing this representation and provide a detailed description of a PGA along with discussion of its benefits and drawbacks. We compare the behavior of a PGA with that of a canonical GA (CGA) and discuss conclusions and future work based on these preliminary studies.